
from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowerCamelCase_    :			Optional[int]    =	logging.get_logger(__name__)

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Dict          =			["""audio_values""", """audio_mask"""]

			def __init__(							self			:							Dict    ,						snake_case_			:							Tuple=2048    ,						snake_case_			:							Tuple=1    ,						snake_case_			:							List[Any]=[16, 16]    ,						snake_case_			:							str=128    ,						snake_case_			:							int=4_4100    ,						snake_case_			:							List[str]=86    ,						snake_case_			:							Union[str, Any]=2048    ,						snake_case_			:							int=0.0    ,						**snake_case_			:							Any    ,						):
							super().__init__(
							    feature_size=snake_case_    ,						sampling_rate=snake_case_    ,						padding_value=snake_case_    ,						**snake_case_    ,						)

							UpperCamelCase_:				Dict						=    spectrogram_length
							UpperCamelCase_:				Optional[Any]						=    num_channels
							UpperCamelCase_:				List[Any]						=    patch_size
							UpperCamelCase_:				Union[str, Any]						=    feature_size // self.patch_size[1]
							UpperCamelCase_:				List[Any]						=    n_fft
							UpperCamelCase_:				List[str]						=    sampling_rate // hop_length_to_sampling_rate
							UpperCamelCase_:				List[str]						=    sampling_rate
							UpperCamelCase_:				str						=    padding_value
							UpperCamelCase_:				Dict						=    mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2    ,						num_mel_filters=snake_case_    ,						min_frequency=0.0    ,						max_frequency=2_2050.0    ,						sampling_rate=snake_case_    ,						norm="""slaney"""    ,						mel_scale="""slaney"""    ,						).T

			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							np.array   ):
							UpperCamelCase_:				Optional[Any]						=    spectrogram(
							    snake_case_    ,						window_function(self.n_fft    ,						"""hann"""   )    ,						frame_length=self.n_fft    ,						hop_length=self.hop_length    ,						power=2.0    ,						mel_filters=self.mel_filters.T    ,						log_mel="""dB"""    ,						db_range=80.0    ,						)
							UpperCamelCase_:				Tuple						=    log_spec[:, :-1]
							UpperCamelCase_:				int						=    log_spec - 20.0
							UpperCamelCase_:				Any						=    np.clip(log_spec / 40.0    ,						-2.0    ,						0.0   ) + 1.0
							return log_spec




			def __call__(							self			:							Union[str, Any]    ,						snake_case_			:							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,						snake_case_			:							Optional[Union[str, TensorType]] = None    ,						snake_case_			:							Optional[bool] = True    ,						snake_case_			:							Optional[int] = None    ,						snake_case_			:							bool = False    ,						snake_case_			:							bool = False    ,						**snake_case_			:							Tuple    ,						):

							if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
															raise ValueError(
															    """This feature extractor is set to support sampling rate"""
															    f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
															    f''' with {self.sampling_rate} and not {sampling_rate}.'''   )
							else:
											logger.warning(
											    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
											    """Failing to do so can result in silent errors that might be hard to debug."""   )

							UpperCamelCase_:				List[str]						=    isinstance(snake_case_    ,						np.ndarray   ) and len(raw_speech.shape   ) > 1
							if is_batched_numpy and len(raw_speech.shape   ) > 2:
											raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''   )
							UpperCamelCase_:				str						=    is_batched_numpy or (
							    isinstance(snake_case_    ,						(list, tuple)   ) and (isinstance(raw_speech[0]    ,						(np.ndarray, tuple, list)   ))
							)
							if is_batched:
											UpperCamelCase_:				str						=    [np.asarray([speech]    ,						dtype=np.floataa   ).T for speech in raw_speech]
							elif not is_batched and not isinstance(snake_case_    ,						np.ndarray   ):
											UpperCamelCase_:				Any						=    np.asarray(snake_case_    ,						dtype=np.floataa   )
							elif isinstance(snake_case_    ,						np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
											UpperCamelCase_:				Optional[int]						=    raw_speech.astype(np.floataa   )
							# always return batch
							if not is_batched:
											UpperCamelCase_:				List[Any]						=    [np.asarray([raw_speech]   ).T]

							# Convert audio signals to log mel spectrograms, truncate by time axis
							UpperCamelCase_:				str						=    [
							    self._np_extract_fbank_features(waveform.squeeze()   ).T[: self.spectrogram_length] for waveform in raw_speech
							]
							if isinstance(audio_features[0]    ,						snake_case_   ):
											UpperCamelCase_:				List[Any]						=    [np.asarray(snake_case_    ,						dtype=np.floataa   ) for feature in audio_features]

							# Create audio attention mask
							UpperCamelCase_:				Optional[Any]						=    max(
							    [ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len for feature in audio_features]   )  # The maximum number of audio patches in a batch
							if return_attention_mask:
											UpperCamelCase_:				int						=    [
											    (ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [1]
											    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [0]
											    for feature in audio_features
											]
											UpperCamelCase_:				Optional[Any]						=    np.array(snake_case_   ).astype(np.floataa   )

							# convert into correct format for padding
							UpperCamelCase_:				Optional[Any]						=    max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
							UpperCamelCase_:				Tuple						=    np.ones([len(snake_case_   ), 1, max_time_len, self.feature_size]   ).astype(np.floataa   )
							UpperCamelCase_:				Union[str, Any]						=    padded_audio_features * self.padding_value
							for i in range(len(snake_case_   )   ):
											UpperCamelCase_:				Any						=    audio_features[i]
											UpperCamelCase_:				Any						=    feature

							# return as BatchFeature
							if return_attention_mask:
											UpperCamelCase_:				Union[str, Any]						=    {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
											UpperCamelCase_:				int						=    {"""audio_values""": padded_audio_features}

							UpperCamelCase_:				Optional[int]						=    BatchFeature(data=snake_case_    ,						tensor_type=snake_case_   )
							return encoded_inputs








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :			Union[str, Any]    =	{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			List[Any]    =	[
							    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TapasForMaskedLM""",
							    """TapasForQuestionAnswering""",
							    """TapasForSequenceClassification""",
							    """TapasModel""",
							    """TapasPreTrainedModel""",
							    """load_tf_weights_in_tapas""",
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Optional[Any]    =	[
							    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFTapasForMaskedLM""",
							    """TFTapasForQuestionAnswering""",
							    """TFTapasForSequenceClassification""",
							    """TFTapasModel""",
							    """TFTapasPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Any    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






